Radiology

AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook.

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means f...

Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis.

PURPOSE: To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augmenting algorithm (DLICA) for hypovascular hepatic metastases.

Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT.

OBJECTIVES: The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after N...

Multicentric Ga-PSMA PET radiomics for treatment response assessment of Lu-PSMA-617 radioligand therapy in patients with metastatic castration-resistant prostate cancer.

OBJECTIVE: The treatment with Lutetium PSMA (Lu-PSMA) in patients with metastatic castration-resistant prostate cancer (mCRPC) has recently been appro...

Proton range uncertainty caused by synthetic computed tomography generated with deep learning from pelvic magnetic resonance imaging.

BACKGROUND: In proton therapy, it is disputed whether synthetic computed tomography (sCT), derived from magnetic resonance imaging (MRI), permits accu...

Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.

PURPOSE: Convolutional Neural Networks (ConvNets) have quickly become popular machine learning techniques in recent years, particularly in the classif...

Impact of retraining a deep learning algorithm for improving guideline-compliant aortic diameter measurements on non-gated chest CT.

PURPOSE/OBJECTIVE: Reliable detection of thoracic aortic dilatation (TAD) is mandatory in clinical routine. For ECG-gated CT angiography, automated de...

Evaluation of late gadolinium enhancement cardiac MRI using deep learning reconstruction.

BACKGROUND: Deep learning (DL)-based methods have been used to improve the imaging quality of magnetic resonance imaging (MRI) by denoising.

Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study.

Computational hemodynamics is increasingly being used to quantify hemodynamic characteristics in and around abdominal aortic aneurysms (AAA) in a pati...

Generation of fluoroscopy-alike radiographs as alternative datasets for deep learning in interventional radiology.

In fluoroscopy-guided interventions (FGIs), obtaining large quantities of labelled data for deep learning (DL) can be difficult. Synthetic labelled da...

Pre-screening for non-diagnostic coronary computed tomography angiography.

AIMS: Indiscriminate coronary computed tomography angiography (CCTA) referrals for suspected coronary artery disease could result in a higher rate of ...

Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images.

Magnetic resonance imaging (MRI) is increasingly being used to delineate morphological changes underlying neurological disorders. Successfully detecti...

Deep Learning to Optimize Magnetic Resonance Imaging Prediction of Motor Outcomes After Hypoxic-Ischemic Encephalopathy.

BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard for outcome prediction after hypoxic-ischemic encephalopathy (HIE). Published scorin...

Artificial intelligence in cardiac computed tomography.

Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and al...

Coronary computed tomography angiographic detection of in-stent restenosis via deep learning reconstruction: a feasibility study.

OBJECTIVES: Evaluation of in-stent restenosis (ISR), especially for small stents, remains challenging during computed tomography (CT) angiography. We ...

Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning.

PURPOSE: The axial field of view (AFOV) of a positron emission tomography (PET) scanner greatly affects the quality of PET images. Although a total-bo...

Coupling synthetic and real-world data for a deep learning-based segmentation process of 4D flow MRI.

BACKGROUND AND OBJECTIVE: Phase contrast magnetic resonance imaging (4D flow MRI) is an imaging technique able to provide blood velocity in vivo and m...

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line arti...

Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting.

We propose a deep learning (DL) model and a hyperparameter optimization strategy to reconstruct T and T maps acquired with the magnetic resonance fing...

Deep learning-based reconstruction can improve canine thoracolumbar magnetic resonance image quality and reduce slice thickness.

In veterinary practice, thin-sliced thoracolumbar MRI is useful in detecting small lesions, especially in small-breed dogs. However, it is challenging...